

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DetaImageProcessor



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=7  ,      _lowercase=3  ,      _lowercase=30  ,      _lowercase=400  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase=[0.5, 0.5, 0.5]  ,      _lowercase=[0.5, 0.5, 0.5]  ,      _lowercase=True  ,      _lowercase=1 / 255  ,      _lowercase=True  ,      )     -> Optional[int]:
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									a_						:						List[str] 							= size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
									a_						:						Tuple 							= parent
									a_						:						Any 							= batch_size
									a_						:						int 							= num_channels
									a_						:						Optional[Any] 							= min_resolution
									a_						:						Tuple 							= max_resolution
									a_						:						Dict 							= do_resize
									a_						:						Optional[int] 							= size
									a_						:						Dict 							= do_normalize
									a_						:						List[str] 							= image_mean
									a_						:						List[Any] 							= image_std
									a_						:						Tuple 							= do_rescale
									a_						:						Union[str, Any] 							= rescale_factor
									a_						:						List[Any] 							= do_pad





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									}





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase=False    )     -> Any:
									if not batched:
												a_						:						Any 							= image_inputs[0]
												if isinstance(_lowercase  ,      Image.Image    ):
															a_							,					a_						:						Optional[int] 							= image.size
												else:
															a_							,					a_						:						Any 							= image.shape[1], image.shape[2]
												if w < h:
															a_						:						Optional[int] 							= int(self.size["""shortest_edge"""] * h / w    )
															a_						:						List[str] 							= self.size["""shortest_edge"""]
												elif w > h:
															a_						:						Optional[int] 							= self.size["""shortest_edge"""]
															a_						:						Optional[int] 							= int(self.size["""shortest_edge"""] * w / h    )
												else:
															a_						:						List[str] 							= self.size["""shortest_edge"""]
															a_						:						List[str] 							= self.size["""shortest_edge"""]

									else:
												a_						:						List[Any] 							= []
												for image in image_inputs:
															a_							,					a_						:						Optional[Any] 							= self.get_expected_values([image]    )
															expected_values.append((expected_height, expected_width)    )
												a_						:						List[Any] 							= max(_lowercase  ,      key=lambda _lowercase    : item[0]    )[0]
												a_						:						Tuple 							= max(_lowercase  ,      key=lambda _lowercase    : item[1]    )[1]

									return expected_height, expected_width







@require_torch
@require_vision
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Dict       					=			DetaImageProcessor if is_vision_available() else None





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						int 							= DetaImageProcessingTester(self    )





						@property
						def 			UpperCamelCase__					(    self    )     -> int:
									return self.image_processor_tester.prepare_image_processor_dict()





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[str] 							= self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(_lowercase  ,      """image_mean"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """image_std"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_normalize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_resize"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_rescale"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """do_pad"""    )    )
									self.assertTrue(hasattr(_lowercase  ,      """size"""    )    )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Optional[int] 							= self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size  ,      {"""shortest_edge""": 18, """longest_edge""": 1_333}    )
									self.assertEqual(image_processor.do_pad  ,      _lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									pass





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									# Initialize image_processing
									a_						:						List[str] 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									a_						:						Optional[int] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      Image.Image    )

									# Test not batched input
									a_						:						Dict 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values

									a_							,					a_						:						Union[str, Any] 							= self.image_processor_tester.get_expected_values(_lowercase    )

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									a_							,					a_						:						Union[str, Any] 							= self.image_processor_tester.get_expected_values(_lowercase  ,      batched=_lowercase    )

									a_						:						Tuple 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> Tuple:
									# Initialize image_processing
									a_						:						Dict 							= self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									a_						:						Dict 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      numpify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      np.ndarray    )

									# Test not batched input
									a_						:						List[str] 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values

									a_							,					a_						:						Union[str, Any] 							= self.image_processor_tester.get_expected_values(_lowercase    )

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									a_						:						List[str] 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values

									a_							,					a_						:						Dict 							= self.image_processor_tester.get_expected_values(_lowercase  ,      batched=_lowercase    )

									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





						def 			UpperCamelCase__					(    self    )     -> int:
									# Initialize image_processing
									a_						:						List[str] 							= self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									a_						:						Optional[int] 							= prepare_image_inputs(self.image_processor_tester  ,      equal_resolution=_lowercase  ,      torchify=_lowercase    )
									for image in image_inputs:
												self.assertIsInstance(_lowercase  ,      torch.Tensor    )

									# Test not batched input
									a_						:						Any 							= image_processing(image_inputs[0]  ,      return_tensors="""pt"""    ).pixel_values

									a_							,					a_						:						Tuple 							= self.image_processor_tester.get_expected_values(_lowercase    )

									self.assertEqual(
									    encoded_images.shape  ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,      )

									# Test batched
									a_						:						int 							= image_processing(_lowercase  ,      return_tensors="""pt"""    ).pixel_values

									a_							,					a_						:						Tuple 							= self.image_processor_tester.get_expected_values(_lowercase  ,      batched=_lowercase    )

									self.assertEqual(
									    encoded_images.shape  ,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )  ,      )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									# prepare image and target
									a_						:						Any 							= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
									with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""  ,      """r"""    ) as f:
												a_						:						Optional[int] 							= json.loads(f.read()    )

									a_						:						List[Any] 							= {"""image_id""": 39_769, """annotations""": target}

									# encode them
									a_						:						Any 							= DetaImageProcessor()
									a_						:						int 							= image_processing(images=_lowercase  ,      annotations=_lowercase  ,      return_tensors="""pt"""    )

									# verify pixel values
									a_						:						Optional[Any] 							= torch.Size([1, 3, 800, 1_066]    )
									self.assertEqual(encoding["""pixel_values"""].shape  ,      _lowercase    )

									a_						:						Optional[int] 							= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,      _lowercase  ,      atol=1e-4    )    )

									# verify area
									a_						:						Any 							= torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,      _lowercase    )    )
									# verify boxes
									a_						:						str 							= torch.Size([6, 4]    )
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,      _lowercase    )
									a_						:						Optional[Any] 							= torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,      _lowercase  ,      atol=1e-3    )    )
									# verify image_id
									a_						:						List[str] 							= torch.tensor([39_769]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,      _lowercase    )    )
									# verify is_crowd
									a_						:						Tuple 							= torch.tensor([0, 0, 0, 0, 0, 0]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,      _lowercase    )    )
									# verify class_labels
									a_						:						int 							= torch.tensor([75, 75, 63, 65, 17, 17]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,      _lowercase    )    )
									# verify orig_size
									a_						:						List[Any] 							= torch.tensor([480, 640]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,      _lowercase    )    )
									# verify size
									a_						:						int 							= torch.tensor([800, 1_066]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,      _lowercase    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> str:
									# prepare image, target and masks_path
									a_						:						Dict 							= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
									with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""  ,      """r"""    ) as f:
												a_						:						Union[str, Any] 							= json.loads(f.read()    )

									a_						:						Union[str, Any] 							= {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

									a_						:						Tuple 							= pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""    )

									# encode them
									a_						:						Optional[int] 							= DetaImageProcessor(format="""coco_panoptic"""    )
									a_						:						str 							= image_processing(images=_lowercase  ,      annotations=_lowercase  ,      masks_path=_lowercase  ,      return_tensors="""pt"""    )

									# verify pixel values
									a_						:						str 							= torch.Size([1, 3, 800, 1_066]    )
									self.assertEqual(encoding["""pixel_values"""].shape  ,      _lowercase    )

									a_						:						List[Any] 							= torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]    )
									self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]  ,      _lowercase  ,      atol=1e-4    )    )

									# verify area
									a_						:						Optional[Any] 							= torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]  ,      _lowercase    )    )
									# verify boxes
									a_						:						Optional[int] 							= torch.Size([6, 4]    )
									self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape  ,      _lowercase    )
									a_						:						Any 							= torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]  ,      _lowercase  ,      atol=1e-3    )    )
									# verify image_id
									a_						:						Tuple 							= torch.tensor([39_769]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]  ,      _lowercase    )    )
									# verify is_crowd
									a_						:						str 							= torch.tensor([0, 0, 0, 0, 0, 0]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]  ,      _lowercase    )    )
									# verify class_labels
									a_						:						Dict 							= torch.tensor([17, 17, 63, 75, 75, 93]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]  ,      _lowercase    )    )
									# verify masks
									a_						:						List[str] 							= 822_873
									self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()  ,      _lowercase    )
									# verify orig_size
									a_						:						Any 							= torch.tensor([480, 640]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]  ,      _lowercase    )    )
									# verify size
									a_						:						str 							= torch.tensor([800, 1_066]    )
									self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]  ,      _lowercase    )    )





from __future__ import annotations


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if len(a__) == 0:
						return []
			a_							,					a_						:						List[Any] 							= min(a__), max(a__)
			a_						:						Tuple 							= int(max_value - min_value) + 1
			a_						:						list[list] 							= [[] for _ in range(a__)]

			for i in my_list:
						buckets[int(i - min_value)].append(a__)

			return [v for bucket in buckets for v in sorted(a__)]


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


